A Sinh-Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems.

Area of Science:

• Computational Intelligence
• Metaheuristic Optimization
• Algorithm Development

Background:

• The Dung Beetle Optimization (DBO) algorithm offers strong optimization capabilities but suffers from limitations in population initialization, search speed, and global exploration.
• Existing metaheuristic algorithms require enhancements to address challenges in complex problem-solving.

Purpose of the Study:

• To introduce the Sinh-Cosh Dung Beetle Optimization (SCDBO) algorithm, an enhanced version of DBO.
• To improve the random population initialization, search speed, and global search capabilities of the DBO algorithm.

Main Methods:

• Developed the SCDBO algorithm by integrating Sinh and Cosh functions into the DBO framework.
• Leveraged nonlinear enhancements through Sinh and Cosh functions to optimize initial distribution and balance beetle development.
• Evaluated SCDBO performance against seven benchmark algorithms using CEC2017 test functions.

Main Results:

• SCDBO demonstrated enhanced search efficiency and global exploration capabilities compared to the original DBO.
• The algorithm achieved superior performance on the CEC2017 test suite.
• Successful application to engineering problems including robot arm design, pressure vessel design, and UAV path planning.

Conclusions:

• The SCDBO algorithm effectively overcomes the limitations of the original DBO.
• SCDBO exhibits significant potential for solving complex real-world optimization problems.
• The proposed enhancements provide a robust and efficient metaheuristic approach.